Identification of geochemical anomalies using singularity theory is a topic of interest in the field of mineral exploration. The ordinary singularity mapping technique does not consider the critical role of geological structures in the ore-forming environment. Therefore, models of spatially weighted singularity have been developed through the incorporation of fault systems, which might play an important role in ore-forming processes, and thus, are key factors in spatial analysis of mineralization indicators. In this study, we proposed a strengthened singularity mapping technique that was spatially enhanced and weighted by mineralizationefficient fault systems. For this, we applied distance distribution analysis to distinguish “efficient” (linked to mineralization) and “inefficient” (not linked to mineralization) fault systems, and then, used the former to generate the strengthened spatially weighted singularity models of geochemical indicator elements. The rationale is that faults with different orientations are the result of various geological processes, of which only some are linked to the ore-forming processes. We also integrated the strengthened models of geochemical anomalies by using random forest (RF) and principal component analysis (PCA) techniques to produce a stronger geochemical clue. Comparison of the integration results demonstrated that the former, a strengthened anisotropic geochemical singularity modeling technique proposed in this paper through the incorporation of efficient fault systems and RF method, is superior to the later, an existing anisotropic geochemical singularity modeling technique through the incorporation of all faults and PCA method. Data related to porphyry copper mineralization in Sarduiyeh district, Iran, was used to illustrate the procedure applied.
